| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 4 | | adverbTagCount | 2 | | adverbTags | | 0 | "She stepped back [back]" | | 1 | "he said quietly [quietly]" |
| | dialogueSentences | 110 | | tagDensity | 0.036 | | leniency | 0.073 | | rawRatio | 0.5 | | effectiveRatio | 0.036 | |
| 97.39% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1916 | | totalAiIsmAdverbs | 1 | | found | | | highlights | | |
| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 68.68% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1916 | | totalAiIsms | 12 | | found | | | highlights | | 0 | "grave" | | 1 | "familiar" | | 2 | "weight" | | 3 | "pulse" | | 4 | "predictable" | | 5 | "stomach" | | 6 | "comforting" | | 7 | "silk" | | 8 | "silence" | | 9 | "absolutely" |
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| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 1 | | narrationSentences | 138 | | matches | | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 1 | | narrationSentences | 138 | | filterMatches | (empty) | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 244 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 38 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1916 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 15 | | unquotedAttributions | 1 | | matches | | 0 | "Human hand, she guessed, not claw, not tooth." |
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| 87.65% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 50 | | wordCount | 1283 | | uniqueNames | 12 | | maxNameDensity | 1.25 | | worstName | "Lucien" | | maxWindowNameDensity | 2 | | worstWindowName | "Aurora" | | discoveredNames | | Aurora | 14 | | Moreau | 1 | | Lucien | 16 | | Eva | 5 | | Soho | 1 | | Avaros | 2 | | Ptolemy | 5 | | Latin | 1 | | Greek | 1 | | Silas | 1 | | Yu-Fei | 1 | | London | 2 |
| | persons | | 0 | "Aurora" | | 1 | "Moreau" | | 2 | "Lucien" | | 3 | "Eva" | | 4 | "Ptolemy" | | 5 | "Silas" |
| | places | | 0 | "Soho" | | 1 | "Avaros" | | 2 | "London" |
| | globalScore | 0.876 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 90 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like bone but gleamed like polishe" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1916 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 3 | | totalSentences | 244 | | matches | | 0 | "hated that he" | | 1 | "Hated that he" | | 2 | "knew that name" |
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| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 168 | | mean | 11.4 | | std | 15.36 | | cv | 1.347 | | sampleLengths | | 0 | 21 | | 1 | 3 | | 2 | 58 | | 3 | 5 | | 4 | 5 | | 5 | 4 | | 6 | 3 | | 7 | 3 | | 8 | 8 | | 9 | 9 | | 10 | 2 | | 11 | 3 | | 12 | 19 | | 13 | 7 | | 14 | 3 | | 15 | 10 | | 16 | 14 | | 17 | 35 | | 18 | 8 | | 19 | 2 | | 20 | 3 | | 21 | 7 | | 22 | 33 | | 23 | 69 | | 24 | 12 | | 25 | 5 | | 26 | 7 | | 27 | 20 | | 28 | 14 | | 29 | 9 | | 30 | 98 | | 31 | 5 | | 32 | 3 | | 33 | 2 | | 34 | 8 | | 35 | 6 | | 36 | 6 | | 37 | 3 | | 38 | 5 | | 39 | 1 | | 40 | 43 | | 41 | 45 | | 42 | 2 | | 43 | 4 | | 44 | 1 | | 45 | 32 | | 46 | 3 | | 47 | 7 | | 48 | 4 | | 49 | 4 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 0 | | totalSentences | 138 | | matches | (empty) | |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 211 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 244 | | ratio | 0 | | matches | (empty) | |
| 98.29% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1287 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 54 | | adverbRatio | 0.04195804195804196 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.003108003108003108 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 244 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 244 | | mean | 7.85 | | std | 6.51 | | cv | 0.829 | | sampleLengths | | 0 | 21 | | 1 | 3 | | 2 | 21 | | 3 | 22 | | 4 | 9 | | 5 | 6 | | 6 | 5 | | 7 | 5 | | 8 | 4 | | 9 | 3 | | 10 | 3 | | 11 | 8 | | 12 | 9 | | 13 | 2 | | 14 | 3 | | 15 | 10 | | 16 | 9 | | 17 | 7 | | 18 | 3 | | 19 | 10 | | 20 | 8 | | 21 | 1 | | 22 | 5 | | 23 | 2 | | 24 | 19 | | 25 | 4 | | 26 | 5 | | 27 | 5 | | 28 | 8 | | 29 | 2 | | 30 | 3 | | 31 | 7 | | 32 | 10 | | 33 | 23 | | 34 | 9 | | 35 | 23 | | 36 | 37 | | 37 | 12 | | 38 | 5 | | 39 | 7 | | 40 | 3 | | 41 | 17 | | 42 | 14 | | 43 | 9 | | 44 | 11 | | 45 | 11 | | 46 | 15 | | 47 | 20 | | 48 | 5 | | 49 | 5 |
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| 45.49% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 14 | | diversityRatio | 0.3073770491803279 | | totalSentences | 244 | | uniqueOpeners | 75 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 9 | | totalSentences | 127 | | matches | | 0 | "Then she saw the blood." | | 1 | "Too controlled for panic." | | 2 | "Too neat for an accident." | | 3 | "Then he had kissed her" | | 4 | "Once with contempt." | | 5 | "Once with silence." | | 6 | "Only tiredness dragged thin over" | | 7 | "Then, from the front door," | | 8 | "Then a man’s voice drifted" |
| | ratio | 0.071 | |
| 94.02% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 40 | | totalSentences | 127 | | matches | | 0 | "His charcoal suit looked uncreased" | | 1 | "She kept the chain fastened." | | 2 | "Her grip tightened on the" | | 3 | "She almost shut the door" | | 4 | "Her chain rattled as she" | | 5 | "He shifted his weight onto" | | 6 | "She hated that he knew" | | 7 | "Her jaw set." | | 8 | "She shut the door, slid" | | 9 | "He crossed the threshold with" | | 10 | "She pointed with her chin." | | 11 | "He sat without argument at" | | 12 | "Her own pulse beat too" | | 13 | "She set the tin down" | | 14 | "He shrugged out of the" | | 15 | "She snapped on Eva’s rubber" | | 16 | "She cut through the shirt" | | 17 | "His skin was warm under" | | 18 | "She cleaned around it with" | | 19 | "He watched her work while" |
| | ratio | 0.315 | |
| 46.61% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 105 | | totalSentences | 127 | | matches | | 0 | "The first deadbolt clicked back," | | 1 | "Lucien Moreau stood in the" | | 2 | "His charcoal suit looked uncreased" | | 3 | "The black one gave away" | | 4 | "She kept the chain fastened." | | 5 | "Her grip tightened on the" | | 6 | "Ptolemy slid between her ankles" | | 7 | "Lucien glanced down at the" | | 8 | "She almost shut the door" | | 9 | "A dark smear at his" | | 10 | "Lucien never bled by chance." | | 11 | "Her chain rattled as she" | | 12 | "He shifted his weight onto" | | 13 | "She hated that he knew" | | 14 | "That landed where he meant" | | 15 | "Her jaw set." | | 16 | "She shut the door, slid" | | 17 | "He crossed the threshold with" | | 18 | "Ptolemy backed up, tail puffed," | | 19 | "Aurora locked all three deadbolts" |
| | ratio | 0.827 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 127 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 3 | | matches | | 0 | "He set it on the table beside Ptolemy, who sniffed it and flattened his ears." | | 1 | "The packet was old parchment, not modern paper, marked with red-brown ink that had dried in jagged strokes along the fold." | | 2 | "Inside lay a single strip of vellum and a black key no longer than her little finger, carved from something that looked like bone but gleamed like polished ston…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 4 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 1 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 110 | | tagDensity | 0.009 | | leniency | 0.018 | | rawRatio | 0 | | effectiveRatio | 0 | |